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At least 55 records · Page 3

One Engine Inoperative (OEI) and Autorotation For Heavy Lift Rotorcraft Systems

Federal Aviation Administration will certainly require the Heavy Lift Rotorcraft to be operated under Category A performance and operations requirements. Because of the weight, no operation will be allowed except Category A according to FAA Part 29.1(c). This means that any where along the flight path, the aircraft must be able to land safely following an engine failure or continue flight. A repeatable flight profile must be developed and executed to ensure that the aircraft can be safely landed or flown away depending on its location on the flight profile. This means that there will be no Height-Velocity testing required as is currently required for Part 29 Category B. Since all the configurations shown to date are different than existing rotorcraft, each type would have to develop their individual requirements under existing special conditions FAA Part 21.17(b). This means the FAA will take the opportunity to negotiate additional requirements or change requirements to ensure safety. For example, since the tiltrotor did not fit normal rotorcraft category, new rules were negotiated between the applicant and the FAA. As a result of this negotiation, performance requirements for Category A were increased. The rules were written in terms of guaranteed performance instead of Category A requirements. Detailed discussion will follow later. The proposed tiltrotor would likely follow along with the current tiltrotor rules with the possibility of increase Category A performance requirements. Compounding with addition of wing and auxiliary thrust to both the tandem and coaxial rotor would result in new special condition aircraft. To my knowledge, no compound tandem or compound coaxial rotor has ever been certified by FAA.

Wood, Tom↗

The Intelligent Landing System for Safe and Precise Landing on Europa

Europa, the smallest of Jupiter’s Galilean moons, is thought to harbor a vast liquid water ocean beneath its icy crust, making it one of the most scientifically intriguing targets for a robotic surface sampling mission in our Solar System. However, autonomously landing a spacecraft safely and precisely on Europa poses unique challenges, such as very little existing high-resolution reconnaissance imagery, a surface expected to be very rough and hazardous over a wide range of scales, an extremely intense ionizing radiation environment, and very limited lander resources for mass and volume. To address these challenges, we propose a novel Intelligent Landing System (ILS) combining four Guidance, Navigation & Control (GN&C) sensing functions – velocimetry, altimetry, map-relative localization, and hazard detection – that would together enable safe and precise landing on Europa’s surface. The ILS is a smart sensor system, combining an inertial measurement unit (IMU), a monocular, passive-optical camera, and a light detection and ranging (Li-DAR) sensor with dedicated computing resources as well as an onboard 3D terrain map. The ILS leverages more than a decade of technology development from programs such as the Lander Vision System, currently baselined on the Mars 2020 mission. This paper provides a detailed description of the proposed ILS architecture and concept of operations, as well as select preliminary simulation results to assess performance and robustness.

Trawny, Nikolas↗

Hazard Boresight Relative Navigation for Safe Lunar Landing

In the area of planetary landing, hazard detection and avoidance is the act of driving a vehicle to a safe landing area using onboard resources. A hazard detection sensor is used to scan the terrain and these measurements are evaluated to determine where the safe landing sites are located. The selected site is generally not the same as the nominal target, so the vehicle must divert to the new site. This activity involves the interaction between several components, including a suite of onboard GNC algorithms that work together to efficiently choose and divert to a new site. This paper presents the Hazard Boresight Relative Navigation concept, which is a method that provides a common interface between the hazard scan, safe-site selection algorithm, size of the target-relative landing ellipse, divert offset distance and guidance targeting algorithm. After the safe-site is selected from the hazard scan, the original inertial target is replaced with a vehicle-relative target, which is initialized by a measurement from the hazard scan. The new target-relative position state is estimated over time in the navigation filter, and is fed to the guidance algorithm to perform the divert maneuver. In addition to detailing the Hazard Boresight Relative Navigation concept, this paper also presents some general landing terms that can be used in the greater discussion, as well as analysis on how to estimate and predict the vehicle footprint dispersion ellipse during flight, which is used in the safe-site selection algorithm.

Stephen R. Steffes↗

Hazard Boresight Relative Navigation for Safe Lunar Landing

In the area of planetary landing, hazard detection and avoidance is the act of driving a vehicle to a safe landing area using onboard resources. A hazard detection sensor is used to scan the terrain and these measurements are evaluated to determine where the safe landing sites are located. The selected site is generally not the same as the nominal target, so the vehicle must divert to the new site. This activity involves the interaction between several components, including a suite of onboard GNC algorithms that work together to efficiently choose and divert to a new site. This paper presents the Hazard Boresight Relative Navigation concept, which is a method that provides a common interface between the hazard scan, safe-site selection algorithm, size of the target-relative landing ellipse, divert offset distance and guidance targeting algorithm. After the safe-site is selected from the hazard scan, the original inertial target is replaced with a vehicle-relative target, which is initialized by a measurement from the hazard scan. The new target-relative position state is estimated over time in the navigation filter, and is fed to the guidance algorithm to perform the divert maneuver. In addition to detailing the Hazard Boresight Relative Navigation concept, this paper also presents some general landing terms that can be used in the greater discussion, as well as analysis on how to estimate and predict the vehicle footprint dispersion ellipse during flight, which is used in the safe-site selection algorithm.

Stephen Steffes↗

Flight Testing a Real-Time Hazard Detection System for Safe Lunar Landing on the Rocket-Powered Morpheus Vehicle

The Hazard Detection System (HDS) is a component of the ALHAT (Autonomous Landing and Hazard Avoidance Technology) sensor suite, which together provide a lander Guidance, Navigation and Control (GN&C) system with the relevant measurements necessary to enable safe precision landing under any lighting conditions. The HDS consists of a stand-alone compute element (CE), an Inertial Measurement Unit (IMU), and a gimbaled flash LIDAR sensor that are used, in real-time, to generate a Digital Elevation Map (DEM) of the landing terrain, detect candidate safe landing sites for the vehicle through Hazard Detection (HD), and generate hazard-relative navigation (HRN) measurements used for safe precision landing. Following an extensive ground and helicopter test campaign, ALHAT was integrated onto the Morpheus rocket-powered terrestrial test vehicle in March 2014. Morpheus and ALHAT then performed five successful free flights at the simulated lunar hazard field constructed at the Shuttle Landing Facility (SLF) at Kennedy Space Center, for the first time testing the full system on a lunar-like approach geometry in a relevant dynamic environment. During these flights, the HDS successfully generated DEMs, correctly identified safe landing sites and provided HRN measurements to the vehicle, marking the first autonomous landing of a NASA rocket-powered vehicle in hazardous terrain. This paper provides a brief overview of the HDS architecture and describes its in-flight performance.

Avoidance↗

Camera Image Transformation and Registration for Safe Spacecraft Landing and Hazard Avoidance

Inherent geographical hazards of Martian terrain may impede a safe landing for science exploration spacecraft. Surface visualization software for hazard detection and avoidance may accordingly be applied in vehicles such as the Mars Exploration Rover (MER) to induce an autonomous and intelligent descent upon entering the planetary atmosphere. The focus of this project is to develop an image transformation algorithm for coordinate system matching between consecutive frames of terrain imagery taken throughout descent. The methodology involves integrating computer vision and graphics techniques, including affine transformation and projective geometry of an object, with the intrinsic parameters governing spacecraft dynamic motion and camera calibration.

Jones, Brandon M.↗

Lightweight Multifunctional Planetary Probe for Extreme Environment Exploration and Locomotion

The demand to explore new worlds requires the development of advanced technologies that enable landed science on uncertain terrains or in hard to reach locations. As a result, contemporary Entry, Descent, Landing, (EDL) and additional locomotion (EDLL) profiles are becoming increasingly more complex, with the introduction of lifting/guided entries, hazard avoidance on descent, and a plethora of landing techniques including airbags and the skycrane maneuver. The inclusion of each of these subsystems into a mission profile is associated with a substantial mass penalty. This report explores the new all-in-one entry vehicle concept, TANDEM, a new combined EDLL concept, and compares it to the current state of the art EDL systems. The explored system is lightweight and collapsible and provides the capacity for lifting/guided entry, guided descent, hazard avoidance, omnidirectional impact protection and surface locomotion without the aid of any additional subsystems. This Phase I study explored: 1. The capabilities and feasibility of the TANDEM concept as an EDLL vehicle. 2. Extensive impact analysis to ensure mission success in unfavorable landing conditions, and safe landing in Tessera regions. 3. Development of a detailed design for a conceptual mission to Venus. As a result of our work it was shown that: 1. TANDEM provides additional benefits over the Adaptive, Deployable Entry Placement Technology (ADEPT) including guided descent and surface locomotion, while reducing the mass by 38% compared to the ADEPT-VITaL mission. 2. Demonstrated that the design of tensegrity structures, and TANDEM specifically, grows linearly with an increase in velocity, which was previously unknown. 3. Investigation of surface impact revealed a promising results that suggest a properly configured TANDEM vehicle can safely land and preform science in the Tessera regions, which was previously labeled by the Decadal Survey as, largely inaccessible despite its high scientific interest. This work has already resulted in a NASA TM and will be submitted to the Journal of Spacecraft and Rockets.

landing techniques↗

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

Kento Tomita↗

Active Collision Avoidance for Planetary Landers

Present day robotic missions to other planets require precise, a priori knowledge of the terrain to pre-determine a landing spot that is safe. Landing sites can be miles from the mission objective, or, mission objectives may be tailored to suit landing sites. Future robotic exploration missions should be capable of autonomously identifying a safe landing target within a specified target area selected by mission requirements. Such autonomous landing sites must (1) 'see' the surface, (2) identify a target, and (3) land the vehicle. Recent advances in radar technology have resulted in small, lightweight, low power radars that are used for collision avoidance and cruise control systems in automobiles. Such radar systems can be adapted for use as active hazard avoidance systems for planetary landers. The focus of this CIF proposal is to leverage earlier work on collision avoidance systems for MSFC's Mighty Eagle lander and evaluate the use of automotive radar systems for collision avoidance in planetary landers.

Rickman, Doug↗

The role of simulation in determining safe aircraft landing separation criteria

The role of flight simulation in determining safe aircraft landing separation criteria is reviewed and discussed. A broad conclusion is made that previous vortex-encounter simulations were useful for predicting the general response of an aircraft in the presence of trailing vortices and the type of separation criteria to emphasize. These simulations, however, were generally limited in scope and validation. Broad requirements for an accepted simulation methodology are presented. Key technological issues are addressed, including the addition of high-fidelity vortex models and aircraft/vortex interaction effects in the simulation, and validation of simulations with experimental data. Finally, results from a preliminary one-degree-of-freedom simulation are shown. These indicate that reduced landing spacings may be feasible and that current aircraft categorizations should be reviewed.

Stuever, Robert A.↗

GN and C Subsystem Concept for Safe Precision Landing of the Proposed Lunar MARE Robotic Science Mission

The Lunar MARE (Moon Age and Regolith Explorer) Discovery Mission concept targets delivery of a science payload to the lunar surface for sample collection and dating. The mission science is within a 100-meter radius region of smooth lunar maria terrain near Aristarchus crater. The location has several small, sharp craters and rocks that present landing hazards to the spacecraft. For successful delivery of the science payload to the surface, the vehicle Guidance, Navigation and Control (GN&C) subsystem requires safe and precise landing capability, so design infuses the NASA Autonomous precision Landing and Hazard Avoidance Technology (ALHAT) and a gimbaled, throttleable LOX/LCH4 main engine. The ALHAT system implemented for Lunar MARE is a specialization of prototype technologies in work within NASA for the past two decades, including a passive optical Terrain Relative Navigation (TRN) sensor, a Navigation Doppler Lidar (NDL) velocity and range sensor, and a Lidar-based Hazard Detection (HD) sensor. The landing descent profile is from a retrograde orbit over lighted terrain with landing near lunar dawn. The GN&C subsystem with ALHAT capabilities will deliver the science payload to the lunar surface within a 20-meter landing ellipse of the target location and at a site having greater than 99% safety probability, which minimizes risk to safe landing and delivery of the MARE science payload to the intended terrain region.

MARE (Moon Age and Regolith Explorer)↗

Interfacing and Verifying ALHAT Safe Precision Landing Systems with the Morpheus Vehicle

The NASA Autonomous precision Landing and Hazard Avoidance Technology (ALHAT) project developed a suite of prototype sensors to enable autonomous and safe precision landing of robotic or crewed vehicles under any terrain lighting conditions. Development of the ALHAT sensor suite was a cross-NASA effort, culminating in integration and testing on-board a variety of terrestrial vehicles toward infusion into future spaceflight applications. Terrestrial tests were conducted on specialized test gantries, moving trucks, helicopter flights, and a flight test onboard the NASA Morpheus free-flying, rocket-propulsive flight-test vehicle. To accomplish these tests, a tedious integration process was developed and followed, which included both command and telemetry interfacing, as well as sensor alignment and calibration verification to ensure valid test data to analyze ALHAT and Guidance, Navigation and Control (GNC) performance. This was especially true for the flight test campaign of ALHAT onboard Morpheus. For interfacing of ALHAT sensors to the Morpheus flight system, an adaptable command and telemetry architecture was developed to allow for the evolution of per-sensor Interface Control Design/Documents (ICDs). Additionally, individual-sensor and on-vehicle verification testing was developed to ensure functional operation of the ALHAT sensors onboard the vehicle, as well as precision-measurement validity for each ALHAT sensor when integrated within the Morpheus GNC system. This paper provides some insight into the interface development and the integrated-systems verification that were a part of the build-up toward success of the ALHAT and Morpheus flight test campaigns in 2014. These campaigns provided valuable performance data that is refining the path toward spaceflight infusion of the ALHAT sensor suite.

Carson, John M., III↗

Analysis of Flash Lidar Field Test Data for Safe Lunar Landing

In May 2008, the Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project conducted a helicopter field test of a commercial flash lidar to assess its applicability to safe lunar landing. The helicopter flew several flights, which covered a variety of slant ranges and viewing angles, over man-made and natural lunar-like terrains. The collected data were analyzed to assess the performance of the sensor and the performance of two algorithms: Hazard Detection (HD) and Hazard Relative Navigation (HRN). The collected flash lidar data were also used to validate a high fidelity flash lidar software model used in ALHAT Monte Carlo simulations. The field test results, combined with prior simulation results, advanced the technology readiness level of the HD algorithm to TRL 5 and the HRN algorithm to TRL 4.

hazard relative navigation↗

Precision Landing and Hazard Avoidance Doman

The Precision Landing and Hazard Avoidance (PL&HA) domain addresses the development, integration, testing, and spaceflight infusion of sensing, processing, and GN&C functions critical to the success and safety of future human and robotic exploration missions. PL&HA sensors also have applications to other mission events, such as rendezvous and docking. Autonomous PL&HA builds upon the core GN&C capabilities developed to enable soft, controlled landings on the Moon, Mars, and other solar system bodies. Through the addition of a Terrain Relative Navigation (TRN) function, precision landing within tens of meters of a map-based target is possible. The addition of a 3-D terrain mapping lidar sensor improves the probability of a safe landing via autonomous, real-time Hazard Detection and Avoidance (HDA). PL&HA significantly improves the probability of mission success and enhances access to sites of scientific interest located in challenging terrain. PL&HA can also utilize external navigation aids, such as navigation satellites and surface beacons. Advanced Lidar Sensors High precision ranging, velocimetry, and 3-D terrain mapping Terrain Relative Navigation (TRN) TRN compares onboard reconnaissance data with real-time terrain imaging data to update the S/C position estimate Hazard Detection and Avoidance (HDA) Generates a high-resolution, 3-D terrain map in real-time during the approach trajectory to identify safe landing targets Inertial Navigation During Terminal Descent High precision surface relative sensors enable accurate inertial navigation during terminal descent and a tightly controlled touchdown within meters of the selected safe landing target.

Robertson, Edward A.↗

Hazard Boresight Relative Navigation for Safe Lunar Landing

Hazard Boresight Relative Navigation greatly simplifies Hazard Detection and Avoidance methodologies by providing a common interface between the Hazard DEM, the Safe Site Selection Algorithm, the size of the landing ellipse, the divert distance and the Guidance targeting algorithm. After the Safe Site is selected from the DEM, Hazard Boresight Relative Navigation will replace the original planet target with the boresight target. The location of the safe site relative to the boresight is sent back to GNC for the divert maneuver.

Stephen R. Steffes↗